High efficiency of nitric acid controls in alleviating particulate nitrate in livestock and urban areas in South Korea

Abstract

Remarkably, enhanced particulate nitrate (NO₃⁻) concentrations occur in many environments during particulate matter (PM) pollution; however, information on the formation mechanism and alleviation strategies is still limited. Herein, to explore the NO₃⁻ formation mechanism and conditions, we measured the concentrations of water-soluble inorganic ions in PM_1.0 as well as the inorganic gas concentrations of HNO₃, NO₂, and NH₃ in Gimje, a highly dense livestock area, from June to July 2020 and January to February 2021. At the monitoring site, extremely high atmospheric NH₃ was measured with an hourly average of 96.9 ± 48.1 ppb, and the daily average of HNO₃ and PM_1.0 was 0.7 ± 0.7 ppb, and 20.1 ± 8.8 μg m⁻³, respectively. A clear increase in the NO₃⁻ concentration in PM_1.0 was observed on high pollution days (PM_1.0 ≥ 20 μg m⁻³), suggesting that HNO₃ and NH₃ contributed to NO₃⁻ formation. Moreover, we applied the thermodynamic model ISORROPIA-II to predict the NO₃⁻ response to the reduction of total HNO₃ (TN), total NH₃ (TA), and SO₄²⁻. The results showed that controlling TN could be more effective in alleviating particulate NO₃⁻ than controlling SO₄²⁻ and TA in the livestock area. We also compared this result to that of a nearby urban area, Jeonju. A similar result was observed, with efficient HNO₃ control, which reduced the NO₃⁻ concentration in Jeonju. These measurements and simulations indicated that NO_x control could be the most effective approach to reduce particulate NO₃⁻ concentrations in both livestock and urban areas. Our results provide a significant contribution to developing a strategy for alleviating particulate NO₃⁻ pollution.

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Environmental significance

The particulate nitrate (NO₃⁻) concentration is often enhanced during particulate matter (PM) pollution; however, there is limited information on the formation mechanism and reduction strategies of particulate NO₃⁻. Using thermodynamic model ISORROPIA-II and the measurements of inorganic gaseous species and water-soluble ions of PM_1.0, we show that controlling total nitrate could be more effective in reducing particulate NO₃⁻ and PM concentrations than controlling SO₄²⁻ and total ammonia in a livestock area. Moreover, a similar result was observed with efficient HNO₃ control, which reduced NO₃⁻ and PM concentrations in a nearby urban area. This suggests that NO_x control is an effective approach to reduce particulate NO₃⁻ in both livestock and urban areas. In the future, a strategy can be developed to reduce NO₃⁻ pollution by using the results of this study.

Introduction

Particulate matter (PM) below 2.5 μm in aerodynamic diameter (PM_2.5) significantly impacts the air quality, global climate, and human health.^1–3 PM_2.5 is emitted directly into the atmosphere. PM_2.5 mainly contains ammonium sulfate ((NH₄)₂SO₄), ammonium nitrate (NH₄NO₃), and organic aerosols formed via photochemical reactions of precursor gaseous species such as nitrogen oxides (NO_x), ammonia (NH₃), sulfur dioxide (SO₂), and volatile organic compounds.⁴ In PM_2.5, secondary inorganic aerosols (SIAs) account for more than 80% by mass, depending on the location and season.^5,6

Among SIAs, the concentration of NH₄NO₃ remarkably increases during PM_2.5 pollution in various environments.^7–9 NH₄NO₃ can be produced by a series of chemical reactions of NO_x and NH₃, as follows:⁴

NO₂ (g) + OH (g) → HNO₃ (g) (R1)

NH₃ (g) + HNO₃ (g) → NH₄NO₃ (s, aq) (R2)

In the atmosphere, nitrogen dioxide (NO₂) reacts with hydroxyl radicals (OH) to produce nitric acid (HNO₃). HNO₃ then reacts with NH₃ to form NH₄NO₃. NH₄NO₃ is a semi-volatile species that can partition primarily from the gas-phase to the particle-phase, depending on the temperature.⁴

Studies on night-time chemistry have addressed nitrate (NO₃⁻) formation by heterogeneous reactions.¹⁰ During night-time, nitrogen trioxide (NO₃) is produced by the reaction of NO₂ and ozone (O₃) (R3), and the NO₃ reacts with NO₂ to form dinitrogen pentoxide (N₂O₅) (R4). Heterogeneous uptake of N₂O₅ on aerosol surfaces produces HNO₃via hydrolysis (R5).⁴

NO₂ (g) + O₃ (g) → NO₃ (g) + O₂ (g) (R3)

NO₃ (g) + NO₂ (g) → N₂O₅ (g) (R4)

N₂O₅ (g) + H₂O (l) → 2HNO₃ (aq) (R5)

These reactions produce particulate NO₃⁻ under NH₃-rich conditions.⁴ However, under NH₃-poor conditions, HNO₃ can be formed through the reaction of other alkaline species.¹¹

NO_x and NH₃, the main precursor gases of NH₄NO₃, are emitted into the atmosphere by various sources. NO_x is emitted not only by anthropogenic activities such as combustion of oil and coal, energy generation, on-road transportation, and agricultural activities, but also by natural sources such as soil, biomass burning, and lightning.^12,13 In California¹⁴ and the North China Plain,¹⁵ where there are active agricultural areas, the total NO_x emissions accounted for more than 30% in 2013 and 50% in 2017. NH₃ is mainly emitted from agricultural sources, such as fertilizer application, soil, and livestock, accounting for approximately 90% of the total global NH₃ emission,^12,16 which has dramatically increased by 80% from 1970 to 2017.¹²

Studies have been conducted to understand the formation mechanism and develop effective reduction strategies for NH₄NO₃ pollution. Some modeling studies have suggested that reduction of total NH₃ (TA) could be the most effective method to reduce PM concentrations.^9,17 For instance, Franchin et al. (2018) used ISORROPIA-II modeling showing that a reduction in the NH₃ concentration by approximately 50% could decrease the PM_1.0 concentration by ∼36% in a rural area in Utah, United States. Moreover, in central China, which is surrounded by rural and urban areas, GEOS-Chem modeling results showed that a reduction in NH₃ emissions by 47% reduced the PM_2.5 concentrations by ∼9% in July and ∼11% in January.¹⁷ By contrast, other modeling studies have shown that reducing NO_x emissions would be more effective than reducing NH₃ emissions for controlling PM.^18–20 Guo et al. (2018) showed that controlling NO_x emission would effectively reduce NO₃⁻ concentrations in an agricultural area in Cabauw, Netherlands. Furthermore, Chen et al. (2014) suggested that a 50% reduction in NO_x emissions would decrease PM_2.5 and NO₃⁻ concentrations by more than 24% and 42%, respectively, in an urban area in Bakersfield, USA. Analysis of ground-based data showed that NH₄NO₃ formation in the Salt Lake Valley is likely to be total HNO₃ (TN)-limited, and, thus, reduction of NO_x is effective in improving the air quality.²⁰ However, scientific data on highly efficient control strategies of precursor gas in alleviating particulate NO₃⁻ in different areas are still lacking.

In this study, to determine NH₄NO₃ formation and related PM pollution conditions, two intensive field measurements of NH₃, NO₂, HNO₃, and water-soluble inorganic ions (WSIIs) in PM_1.0 were performed in the summer in Gimje, a rural area in South Korea. Gimje is a highly dense livestock area with populated livestock facilities and farms.²¹ Extremely high NH₃ emission²² and PM pollution²³ have been reported in this region. There are almost no studies on the temporal distribution of WSIIs and their precursor gases. In this study, the WSIIs and inorganic gas-particle-phase partitioning of NH₄NO₃ were analyzed by exploring the WSIIs and inorganic gases. Based on the measurement dataset and thermodynamic model ISORROPIA-II, we discuss the effective implementation of PM and NO₃⁻ reduction strategies in livestock-dense areas. Moreover, we compared the results acquired from the rural area with those from a nearby urban area, Jeonju, via thermodynamic modeling by using the NH₃ and WSII concentrations in PM_2.5 data measured from May 2019 to April 2020 by Park et al. (2021).⁸

Methods

Measurements

Particulate WSIIs and inorganic gases were measured in Gimje, Jeollabuk-do, South Korea (35.8395° N, 126.9889° E) (Fig. 1). The monitoring site has livestock complexes for raising pigs and chickens and large-scale farms with open manure storage facilities,^21,24 resulting in high NH₃ emissions.²²

Fig. 1 Map of the measurement site in livestock and urban areas, Jeollabuk-do, South Korea.

Two intensive field measurements were performed during the summer (June to July 2020) and winter (January to February 2021). In the livestock area, concentrations of inorganic gases such as NH₃, NO_x (NO, and NO₂), and HNO₃, and WSIIs in PM_1.0 (Na⁺, NH₄⁺, Mg²⁺, Ca²⁺, K⁺, NO₃⁻, SO₄²⁻, and Cl⁻) were measured. Atmospheric NH₃ was measured every 1 s using a real-time NH₃ instrument (DLT-100, Los Gatos Research, USA) using off-axis integrated cavity output spectroscopy. In principle, the NH₃ instrument using specific wavelengths does not require external calibration.²⁵ However, we performed calibration to confirm the performance of the instrument by mixing standard NH₃ (9.2 ppm, with an accuracy of ±2%, Air Korea, Korea) and N₂ (99.999%, Air Korea, Korea) gases before and after field measurements, resulting in an R² of 0.9999 (Fig. S1†). The detailed procedure for the calibration and measurement of atmospheric NH₃ is described by Park et al. (2021). For the analysis, we used hourly averaged concentrations of atmospheric NH₃. The NO_x concentration was also monitored every 1 m using a NO_x instrument (Serinus 40 Oxides of Nitrogen, Ecotech, Australia), based on the chemiluminescence method. Before and after conducting the field measurements, calibration was conducted by mixing standard NO (4.9 ppm, with an accuracy of ±10%, Air Korea, Korea) and N₂ (99.999%, Air Korea, Korea) gases at four different concentrations (20, 15, 12, and 0 ppb), which resulted in an error concentration of ∼3%. The hourly averaged concentration of atmospheric NO_x was used for the analysis.

PM_1.0 and HNO₃ were collected using a two-stage sequential air sampler (APM Engineering, PMS-114, Korea) which has been widely used.^26,27 The sequential air sampler consisted of two stages for collection of PM_1.0 on a Teflon filter (PTFE, 66155, PALL, USA) at the first stage, and of HNO₃ on a nylon filter (Nylon, 1213776, GVS, USA) at the second stage. It was operated at a flow rate of 16.7 L min⁻¹ for 24 h from 00:00 am to 00:00 am the following day during the measurement period. During the sampling, some semi-volatile species (i.e. NO₃⁻, NH₄⁺ and HNO₃) could be evaporated, leaving approximately ∼10% of their total mass on the filters.^28,29 A total of 84 samples for PM_1.0 (42 samples) and HNO₃ (42 samples) were collected during all measurements. Subsequently, all filter samples were stored at −4 °C in a freezer and then analyzed within two weeks after collection. The PM_1.0 mass concentration collected from the Teflon filter was observed based on the procedure of the method of the USA Environmental Protection Agency.³⁰ To analyze the HNO₃ and WSIIs of PM_1.0, each filter sample was extracted in purified water (18.2 MΩ cm, Merck Milli-Q®, Millipore, Burlington, MA, USA). A detailed extraction procedure was described previously.³¹ The extracts from nylon filters were analyzed to quantify the concentration of HNO₃, and the extracts from Teflon filters were analyzed to determine five cations (Na⁺, NH₄⁺, Mg²⁺, Ca²⁺, and K⁺) and three anions (NO₃⁻, SO₄²⁻, and Cl⁻) using ion chromatography (Aquion, Thermo Scientific, USA). The method detection limits (MDLs) of Na⁺, NH₄⁺, Ca²⁺, Mg²⁺, K⁺, NO₃⁻, SO₄²⁻, and Cl⁻ were 0.01, 0.01, 0.03, 0.01, 0.02, 0.02, 0.03 and 0.02 μg m⁻³, respectively. For gaseous species, the MDLs of HNO₃, NO_x, and NH₃ were 0.01, 0.4,³² and 0.5 (ref. 25) ppb, respectively. Hourly averaged meteorological parameters, including relative humidity (RH), temperature, wind direction, precipitation, and wind speed, were obtained during the measurement period from the Korea Meteorological Administration in Jeonju.³³

Thermodynamic modeling

To estimate whether HNO₃ or NH₃ is the limiting factor for particulate NO₃⁻ formation and to calculate aerosol liquid water content (ALWC), we used ISORROPIA-II, an extensively applied thermodynamic equilibrium model based on a Na⁺–Cl⁻–Ca²⁺–K⁺–Mg²⁺–SO₄²⁻–NH₄⁺–NO₃⁻–H₂O aerosol system.^18,34,35 As model inputs, the measured concentrations of TA (particle-phase NH₄⁺ + gas-phase NH₃), TN (particle-phase NO₃⁻ + gas-phase HNO₃), Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, and SO₄²⁻ and meteorological parameters in the rural area were used. ALWC was also calculated using measured data of inorganic gases, WSIIs, temperature, and RH.³⁵ It was reported that a correlation coefficient between the simulated ISORROPIA-II ALWC and the measured hygroscopic growth factors agreed well at ∼0.89; however, the uncertainty of the predicted ALWC in aerosols would be ∼20% compared to the observed ALWC.³⁶ This underestimation might be due to the unidentified organic species information in ISORROPIA-II.^35,36

In this study, we used the “forward” mode, which calculates gas-particle equilibrium partitioning concentrations using the total concentration of species because this mode provides substantially better computational results for the concentrations of inorganic gases than the reverse mode.^18,37 The mean RH of 74.7 ± 11.3% observed throughout the monitoring site was below the deliquescence relative humidity (DRH) for pure (NH₄)₂SO₄.⁴ However, PM_1.0 could still absorb water below the DRH of the pure salt because PM is a complex mixture of various inorganic salts and organic compounds.^37–39 Therefore, we used the “metastable state” solution, assuming that the aerosol compositions are an aqueous supersaturated solution that prevents the formation of a solid precipitate.^35,37

To compare the phenomena of gas-particle partitioning between rural and urban areas, we also calculated the limiting factor of particulate NO₃⁻ formation and gas-particle partitioning using previous data (measured from May 2019 to April 2020) of WSIIs in PM_2.5 and NH₃ measured in Jeonju, an urban area situated approximately 10 km from Gimje.⁸ In this study, HNO₃ measurement data were unavailable for the urban area; thus, the concentration was estimated using the method of Seo et al. (2020).⁴⁰ The uncertainty in the estimated TN was confirmed by comparing the measured TN at the rural site (Fig. S2†). To confirm the thermodynamic equilibrium, we also compared the measured and predicted values of the SNA (SO₄²⁻, NO₃⁻, NH₄⁺) species (Fig. S1 and S3†). Fig. S3† shows the scatter plots of observations against model predictions of the major secondary inorganic aerosol (SIA) species (NH₄⁺, NO₃⁻ and SO₄²⁻) in livestock and urban areas during measurement periods. A good correlation was found between observations and model predictions, suggesting the good performance of ISORROPIA-II.

Conditional probability function (CPF) analysis

To identify the potential regional-scale transport and local emission sources, a conditional probability function (CPF) analysis was conducted using the wind direction, wind speed, and NH₃ and NO₂ concentrations. The CPF estimates a probability that the given source contribution from the given wind speed and wind direction will exceed a threshold criterion. The threshold criterion was set at the 80^th percentile to indicate the directionality of the source.

Results and discussion

Overview of gaseous species and WSIIs

Two intensive measurements of inorganic gaseous species (namely, HNO₃, NO, NO₂, and NH₃) and WSIIs in PM_1.0 were conducted in the highly dense livestock area of Gimje during the summer and winter of 2020–2021. Fig. 2 shows the daily averaged variations in meteorological parameters, gaseous species, and WSIIs in PM_1.0. Over the study period, the daily average temperature was 15.1 ± 9.8 °C, and RH was 74.7 ± 11.3% (Fig. 2A). The mean concentrations of NH₃, HNO₃, NO, and NO₂ were 96.9, 0.7, 2.3, and 21.3 ppb, respectively (Fig. 2B). The atmospheric NH₃ concentration in the populated livestock area was significantly (2–35 times) higher than that in other livestock and urban areas listed in Table 1.^8,27,41–45 This is because of livestock activities, open manure storage facilities, and livestock waste disposal in the region. The HNO₃ concentration at the monitoring site was lower than that reported in Seoul,²⁷ Quzhou, Beijing,⁴¹ and New Delhi⁴² but higher than that reported in Jeonju,⁸ Seolseongmyeon,²⁷ Gucheng,⁴³ Niangon Adjamé⁴⁴ and San Diego⁴⁵ (Table 1).

Fig. 2 Daily averaged variations in (A) temperature (temp.), relative humidity (RH), and precipitation, (B) NH₃, NO₂, NO, and HNO₃ concentrations, and (C) water-soluble ions in PM_1.0 in the livestock area during the measurement period. The light-yellow shadows represent PM_1.0 pollution days (PM_1.0 ≥ 20 μg m⁻³).

Table 1 List of atmospheric average NH₃ and HNO₃ concentrations in various environments

Location	Period	NH3 (unit: ppb)	HNO3 (unit: ppb)	References
a Estimation based on an equation from Seo et al., 2020.
Livestock area
Gimje, Korea	2020.6–2020.7	96.9	0.7	This study
	2021.1–2021.2
Seolseongmyeon, Korea	2009.1–2018.12	50.0	0.4	Sung et al. (2020)^27
Quzhou, China	2011.1–2014.12	31.2	2.7	Xu et al. (2016)^41
Gucheng, China	2013.5–2013.9	36.2	0.2	Meng et al. (2018)^43
Niangon Adjamé, Côte d’Ivoire	2015.12–2016.2	44.0	0.2	Bahino et al. (2018)^44
Urban area
San Diego, United States	2007.2–2012.3	3.3	0.2	Li et al. (2014)^45
Seoul, Korea	2009.1–2018.12	7.5	0.7	Sung et al. (2020)^27
Beijing, China	2011.1–2014.12	17.2	3.2	Xu et al. (2016)^41
New Delhi, India	2017.12–2018.2	33.9	1.0	Acharja et al. (2020)^42
Jeonju, Korea	2019.5–2020.4	10.5	0.2^a	Park et al. (2021)^8

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The daily concentrations of PM_1.0 and its WSIIs are shown in Fig. 2C. The daily averaged PM_1.0 mass concentration ranged from 3.3 to 45.2 μg m⁻³, with an average of 20.1 ± 8.8 μg m⁻³. NO₃⁻, SO₄²⁻, and NH₄⁺ were the major components of PM_1.0, and the daily concentration of NO₃⁻ ranged from 0.4 to 22.0 μg m⁻³ with an average of 4.8 ± 3.9 μg m⁻³. The daily SO₄²⁻ concentrations fluctuated between 0.1 and 7.1 μg m⁻³, with an average of 3.5 ± 1.8 μg m⁻³. The daily NH₄⁺ concentrations varied from 0.2 to 9.4 μg m⁻³ and showed an average of 2.8 ± 1.6 μg m⁻³. Among all WSII species, a significant dominance of NO₃⁻ was observed at the livestock site during the measurement period (Fig. 2C).

All WSIIs, PM_1.0, and inorganic gaseous species, except for SO₄²⁻ and HNO₃, exhibited higher concentrations in the winter (Table S1†). Among the WSIIs, the NO₃⁻concentration was remarkably enhanced from 2.9 μg m⁻³ in the summer to 7.3 μg m⁻³ in the winter (Table S1 and Fig. S4E†). However, the HNO₃ concentration dramatically decreased to an average of 0.1 ppb in the winter (summer: 1.1 ppb) (Table S1 and Fig. S4C†). Theoretically, NH₃ and HNO₃ phases are dependent on temperature and RH.⁴ NH₄NO₃ partitions can occur from the particle-phase to the gas-phase at low RH and high temperature.^4,46 For this reason, NO₃⁻ is mostly present in gaseous HNO₃ during the summer and has low concentrations, whereas NO₃⁻ is partitioned into particle-phase NO₃⁻ during the winter, resulting in higher atmospheric NO₃⁻ concentrations. Our results, which revealed low HNO₃ levels in the summer and high NO₃⁻ levels in the winter, are consistent with previously reported ones.^27,47 NH₃ is generally temperature-dependent. Thus, high NH₃ levels have been reported in the summer.^8,27,48 However, the atmospheric NH₃ concentration was significantly higher in the winter (124.6 ppb) than in the summer (76.0 ppb) (Table S1 and Fig. S4D†). The plausible reasons for this observation are (1) active manure production at the open storage from the livestock farms and (2) dense NH₃ emissions from mechanically ventilated farms with a low ventilation rate in winter in the study area.³¹ Higher NH₃ concentrations have been reported previously in the winter than in other seasons in livestock areas.^31,49

Enhancements of nitrate during PM pollution

To investigate the phenomenon of PM_1.0 pollution in the livestock area, we compared the chemical characteristics of PM_1.0 on clean and pollution days. Based on PM_1.0 mass concentrations, we classified days into clean (daily average PM_1.0 < 20 μg m⁻³) and pollution days (daily average PM_1.0 ≥ 20 μg m⁻³), as suggested in previous studies.^50,51 On clean days, the average concentration of PM_1.0 was 12.1 ± 4.0 μg m⁻³, and WSIIs accounted for 58.2% (7.1 μg m⁻³) of PM_1.0 (Fig. 3A). SO₄²⁻ was the most dominant ion during the clean days, accounting for 37.1% of PM_1.0, followed by NO₃⁻ (29.9%) and NH₄⁺ (21.8%). Out of 42 days, 23 were defined as PM_1.0-pollution days at the monitoring site. During pollution days, the daily averaged concentration of PM_1.0 was 26.7 ± 5.6 μg m⁻³ with a remarkably enhanced NO₃⁻ fraction (41.7%) in PM_1.0 (Fig. 3A), followed by SO₄²⁻ (25.3%) and NH₄⁺ (23.0%). Moreover, the daily average concentrations of NH₃ and HNO₃ increased significantly to 118.9 ppb and 0.8 ppb, respectively, during pollution days when compared with those during clean days (70.1 ppb and 0.5 ppb, respectively) (Fig. 3B and C). These findings suggest that NH₃ and HNO₃ play a critical role in NO₃⁻ formation during high-PM_1.0 events.

Fig. 3 Comparison of (A) water-soluble ions in PM_1.0, (B) HNO₃ concentration, and (C) NH₃ concentrations for entire, clean (PM_1.0 < 20 μg m⁻³), and high pollution (PM_1.0 ≥ 20 μg m⁻³) days.

Fig. S5† illustrates the CPF result during pollution days. A high probability of NH₃ concentration (179 ppb at the 80^th percentile) was observed around the measurement site during pollution days, suggesting that the high levels of NH₃ were mostly affected by local sources around the livestock complexes rather than long-range transport.

PM_1.0 nitrate formation

Fig. 4 shows the relationship between NO₃⁻ and excess-NH₄⁺ molar concentrations. The excess-NH₄⁺ concentration ranged from 6.0 to 434.3 nmol m⁻³, and NO₃⁻ molar concentrations increased with excess-NH₄⁺ molar concentrations during both clean and pollution days, supporting that NO₃⁻ formation always occurred at the monitoring site. The excess-NH₄⁺ and NO₃⁻ molar concentrations showed a significant correlation, with a slope close to 1.0, thereby indicating that the formation of NH₄NO₃ was primarily through the reaction between NH₃ and HNO₃. This is because of the extremely high levels of atmospheric NH₃ at the livestock site during the study period.⁵² Moreover, a shallower slope for both clean (0.76) and pollution (0.83) days was observed for a livestock site, indicating that most of the measured PM_1.0 samples were residual NH₄⁺ associated with species other than NO₃⁻, such as ammonium chloride (NH₄Cl). This implies that there is enough excess NH₄⁺ that it can drive the partitioning of other semi-volatile acids such as HNO₃ and HCl.⁵³

Fig. 4 Scatter plot of molar concentrations of NO₃⁻ and excess-NH₄⁺ in the livestock area during clean and pollution days. The green and red lines represent the regression of the clean and pollution days, respectively.

HNO₃/NH₃ limitation of particulate nitrate formation

In the livestock area, PM pollution with a drastic enhancement of NO₃⁻ occurred under NH₃-rich conditions, indicating that both NH₃ and HNO₃ are critical precursors for PM_1.0 nitrate formation. To suggest a reduction of such high NO₃⁻ concentrations in the livestock area, we calculated the limiting factor for NO₃⁻ formation using the ISORROPIA-II model.^35,54 The measurement data of the average WSII concentrations, temperature, and RH were used as the model input data (Table S1†).

Fig. 5A shows the contour plots of simulated NO₃⁻ concentrations depending on the TA and TN levels at the minimum SO₄²⁻ (∼1 μg m⁻³) and maximum SO₄²⁻ (∼10 μg m⁻³) concentrations, respectively. In this figure, the left side is the TA-limited area and the right side is the TN-limited area, which were calculated using eqn (1) (unit of each species: μmol m⁻³) as follows:⁵⁵

Fig. 5 NO₃⁻ concentrations calculated using the ISORROPIA-II model depending on total nitrate (TN) and total ammonia (TA) concentrations under (a) SO₄²⁻ = 1 μg m⁻³ and (b) SO₄²⁻ = 10 μg m⁻³ in the (A) livestock area and under (a) SO₄²⁻ = 1 μg m⁻³ and (b) SO₄²⁻ = 20 μg m⁻³ in (B) the urban area⁸ during clean and pollution days. Measurement data of TN and TA concentrations during the monitoring periods are shown by black circles (clean days) and pink circles (pollution days).

The measurement data of TA and TN during clean (black circles) and pollution days (pink circles) are shown in Fig. 5. All measured data were TN-limited on both clean and pollution days in all cases of minimum and maximum SO₄⁻ concentrations, given the NH₃-rich concentration in the study area (Fig. 5A). NO₃⁻ formation in livestock can be controlled through HNO₃. Hence, to reduce HNO₃ concentrations, controlling NO_x emissions might be an important approach to reduce NO₃⁻ concentrations and thus improve air quality in livestock areas.

Park et al. (2021) observed remarkable NH₄NO₃ formation during high PM_2.5 events at nearby urban sites. To compare the results of the limiting factor for NO₃⁻ formation in an urban area, we calculated the limiting factor in Jeonju, a nearby urban area, using measurement data from May 2019 to April 2020.⁸ The method and procedure applied for ISORROPIA-II simulation were used for this step as well. The measurement values of the WSIIs in PM_2.5, NH₃, temperature, and RH in the nearby urban area were used as the input data (Table S1†);⁸ however, the HNO₃ concentration was estimated using ISORROPIA-II because of the absence of data (details are given in S1 in the ESI†). The simulation results showed that under low-SO₄²⁻ conditions (∼1 μg m⁻³), all observed data were affected by the TN-limited regime on both clean and pollution days in the urban area (Fig. 5B). In contrast, although under high-SO₄²⁻ (∼20 μg m⁻³), the measured data fell into both TA-limited and TN-limited regimes during clean days, and most of the data fell into the TN-limited regime during pollution days (Fig. 5B). As the daily SO₄²⁻ concentration in the urban area was ∼4.3 μg m⁻³, the TN-limited regime was more reliable in most cases. This shows that high NO₃⁻ production in the urban area during PM_2.5-pollution days was also mainly controlled by HNO₃. Therefore, to alleviate particulate NO₃⁻ in PM in both livestock and nearby urban areas, control of NO_x emissions (which reduces the HNO₃ concentration) could be crucial.

The efficiency of PM reduction via precursor controls

To understand the alleviating efficiency of particulate NO₃⁻ in PM in livestock and urban areas, we performed a sensitivity analysis using ISORROPIA-II, for which the measurement-averaged data were used as input data (Table S1†). Given the abundance of inorganic salts in both areas, we assumed that the SO₄²⁻–NH₄⁺–NO₃⁻–H₂O system is in equilibrium and that particulate NO₃⁻ is formed through the gas-particle partitioning of NH₃ and HNO₃.

First, we attempted to control the TN concentration to study the decrease in particulate NO₃⁻ and PM concentrations caused by HNO₃ reduction in both livestock and urban areas. In Korea, HNO₃ and NO_x are mainly locally contributed rather than long-range transported to form a particle-phase given their lifetimes and concentrations.⁵⁶ During the measurement periods, the CPF results for NO₂ also showed a high probability of high NO₂ concentrations in both livestock and urban areas occurring locally, supporting that the high NO₂ concentration originated near the study area rather than during long-range transport (Fig. S6†).

Fig. 6 shows the sensitivity results for TN control in livestock and urban areas. The PM mass concentrations decreased linearly as TN decreased in both livestock and urban areas (Fig. 6A and B). In the livestock area, a 50% decrease in the TN resulted in a 50% decrease in the particulate NO₃⁻ and an approximately 33% decrease in the PM_1.0 mass concentrations during the entire period as well as during pollution periods (Fig. 6A). Significant responses in NO₃⁻ (50%) and PM_2.5 (∼23%) were observed during an entire day in the urban area, but a higher reduction in NO₃⁻ (50%) and PM_2.5 (31%) was predicted by a 50% decrease in the TN during pollution days (Fig. 6B). Therefore, to decrease particulate NO₃⁻ and PM concentrations in both areas, controlling HNO₃ is a highly efficient approach.

Fig. 6 Thermodynamic ISORROPIA-II model simulations for SO₄²⁻, NO₃⁻, and NH₄⁺ (SNA) concentrations and aerosol liquid water content (ALWC) varied by (a) total nitrate (TN), (b) total ammonia (TA) and (c) SO₄²⁻ concentration inputs in (A) livestock and (B) urban areas. “E” indicates the wet particulate matter (PM) mass concentration during entire days and “P” indicates the wet PM mass concentration during pollution days. “E_{_50%}” and “P_{_50%}” indicate the wet PM mass concentration when TN, TA, and SO₄²⁻ are reduced by up to 50%. The following inputs were used in the model: temperature (livestock: 288.3 K; urban: 288.2 K); relative humidity (livestock: 75%; urban: 64%); and TN, TA, and SO₄²⁻ concentrations (livestock: 6.5, 73.1, and 3.5 μg m⁻³; urban: 4.8, 9.9, and 4.4 μg m⁻³) (Table S1†).

Second, a sensitivity analysis was performed by controlling TA (Fig. 6). Atmospheric NH₃ is generally considered a local contributor because of its short lifetime.⁵⁷ As shown in Fig. 6A and B, a 50% decrease in TA did not respond to the NO₃⁻ and PM concentrations (only ∼1% in the livestock site and ∼2% in the urban site). An ∼87% (livestock) and ∼78% (urban) reduction in TA would be required to effectively decrease PM mass concentrations.

Finally, SO₄²⁻ was controlled during the simulation. SO₄²⁻ does not undergo gas-particle partitioning and is favored in the particle-phase because of its low volatility.⁴ Reportedly, SO₂ can be transported from China to Korea, and therefore, we cannot rule out the possibility of the transported SO₄²⁻.⁵⁸

Illustrated in Fig. 6A and B are the results of SO₄²⁻ control in the two regions. If we assume that SO₄²⁻ is not transported at all to the receptor areas and formed locally by applying to the actual measured concentration of SO₄²⁻ (livestock: 3.5 μg m⁻³ and urban: 4.3 μg m⁻³), only the PM concentration can be found to decrease in the livestock (PM_1.0: ∼20%, NO₃⁻: ∼0.1%, Fig. 6A) and urban areas (PM_2.5: ∼30%, NO₃⁻: ∼0.0%, Fig. 6B) during PM pollution. A similar reduction in PM was observed during the entire period in both areas. However, some SO₄²⁻ may have been transported from outside Korea. A modeling study showed that approximately half of the SO₄²⁻ in PM_2.5 was transported from China to South Korea during 2012–2016.⁵⁸ Accordingly, if we assume that only half of the SO₄²⁻ was locally produced, then a 50% reduction in SO₄²⁻ leads to a reduction in the PM_1.0 and PM_2.5 concentrations in the livestock (PM_1.0: ∼12%, NO₃⁻: ∼0.1%, Fig. 6A) and urban areas (PM_2.5: ∼20%, NO₃⁻: ∼0.0%, Fig. 6B) during PM pollution. On an entire day, a 50% decrease in the SO₄²⁻ concentration could lead to a ∼13% decrease in PM concentrations and ∼0.0% decrease in the particulate NO₃⁻ concentration in the livestock and urban areas. For SO₄²⁻ control, a linear reduction in the SO₄²⁻ concentration, regardless of whether SO₄²⁻ was transported, resulted in a linear decrease in PM mass concentrations but a non-linear decrease in the particulate NO₃⁻ concentration.

Additionally, we investigated the effect of TN, TA, and SO₄²⁻ control on the gas-particle partitioning of HNO₃ and NH₃. Almost ∼96% of the TA remains in the gas-phase in a livestock area when the TN, TA, and SO₄²⁻ were reduced (Fig. S7A, S8A, and S9A†); however, a decrease in the TN and SO₄²⁻ led to more evaporation of particulate NH₄⁺ into more gaseous NH₃ in an urban area (Fig. S7B and S9B†). However, the particle-phase of NO₃⁻ was always the dominant form in both sites (Fig. S7–S9†).

Conclusions

We monitored NH₃, HNO₃, and WSII concentrations in PM_1.0, in a livestock area in Gimje, from June to July 2020 and January to February 2021 to characterize NO₃⁻ formation. During the study periods, the average concentrations of NH₃ and HNO₃ were 96.9 and 0.7 ppb, respectively, and the daily average of PM_1.0 concentration was 20.1 μg m⁻³, with averages of 2.8 μg m⁻³ for NH₄⁺, 4.8 μg m⁻³ for NO₃⁻, and 3.5 μg m⁻³ for SO₄²⁻. On pollution days (PM_1.0 ≥ 20 μg m⁻³), a remarkable increase in the fraction of NO₃⁻ in PM_1.0 and the daily NH₃ and HNO₃ concentrations was observed, suggesting the critical role of NH₃ and HNO₃ in NO₃⁻ formation. The measurements and ISORROPIA-II-simulated results showed that NO₃⁻ formation in the livestock area was always TN-limited owing to its NH₃-rich environment. Recent studies showed that some amount of the water content of organic materials (∼30%) could influence the water content and pH of aerosol particles.^36,59 Further studies are needed to confirm the effect of organic materials on aerosol water content. Recent studies reported that VOC reduction would be efficient in reducing NH₄NO₃ aerosol in some areas.^60–62 To confirm this, further studies are needed with more measurement datasets.

In comparing the effect of reducing particulate NO₃⁻ and PM concentrations by controlling TN, TA, and SO₄²⁻ from two different environments (i.e., livestock and urban areas), we found that reducing TN is more effective than reducing TA and SO₄²⁻ in alleviating particulate NO₃⁻ and PM in both areas. Collectively, our results provide an understanding of the characteristics and formation of NO₃⁻—the most serious aerosol pollutants in Korea—as well as provide scientific data to develop effective PM reduction policies for livestock and urban areas.

Author contributions

M. S. designed this study. H. K., J. P., S. G. K. and M. S. conducted measurements and analysed the data. M. S., H. K., K. P. and S. G. K prepared the manuscript.

Conflicts of interest

There are no conflicts to declare.

Acknowledgements

This research was supported by the Technology Development Program to Solve Climate Changes of the National Research Foundation (NRF) funded by the Korea government (MSIT) (NRF-2019M1A2A2103956), and by the Fine Particle Research Initiative in East Asia Considering National Differences (FRIEND) Project (NRF-2020M3G1A1114548). We thank Sangmin Oh and Junghyeong Ryu for technical support.

Notes and references

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d2ea00051b

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